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Entry-level data analysis scores 8.2 out of 10 for AI exposure, where 10 is most at risk — one of the highest scores in this index. ML and AI engineering scores 6.0. Data science is the field closest to the technology itself, and that has not protected it. If anything, it made it a faster target.
The short answer for parents: this is not the safe technical career it was sold as three years ago. The people building AI systems are reasonably protected. The much larger number of people who query databases, build dashboards and produce analyses are not, and that is where almost all the entry-level jobs are.
Every career in this index is scored 1–10, where 10 is most exposed to AI. Same six factors, same weights, applied identically to a data scientist and a paramedic.
| Data Science track | 2023 | 2025 | Now | 3-yr move | Band |
|---|---|---|---|---|---|
| ML / AI engineer | 5.0 | 5.5 | 6.0 | +1.0 | Moderate |
| Senior data scientist | 5.5 | 6.1 | 6.5 | +1.0 | Mod–High |
| Data engineer | 6.0 | 6.5 | 7.0 | +1.0 | Mod–High |
| Analytics engineer / BI developer | 6.4 | 7.1 | 7.6 | +1.2 | High |
| Entry data analyst | 6.7 | 7.5 | 8.2 | +1.5 | High |
2023 and 2025 figures are reconstructed using current methodology, not archived from past editions.
For scalethe median career in this edition scores around 5.5. Entry-level software development scores 8.1. Licensed engineering scores 4.0. Bedside nursing scores 2.8.
Every track in this field moved at least a full point in three years. No other profession we score has such a uniformly fast-moving set of numbers — because unlike other fields, there is no protected core here for the movement to stop at.
It is doing a great deal of the work already, and the reason is uncomfortable: data work is exactly the shape of task these systems handle best.
Inputs arrive as structured data. Outputs are code, charts and text. There is no physical component, no licensing requirement, and no regulatory body standing between the work and its automation.
How entry-level data analysis rates against each. Ratings are 0–10 on each factor's own terms.
So you can see what the analysis actually looks like.
How much of this job can AI already do? — rated 9.0
There is an intuition worth dismantling here, because a lot of families are relying on it: that working with AI must be safer than working alongside it. That people who understand these systems will be the last displaced by them.
The index says otherwise, and the reason is structural rather than ironic.
Exposure is determined by the shape of the work, not by the subject matter. Our framework asks whether a task takes structured inputs, produces text or code as output, requires no physical presence, involves no licensed accountability, and repeats recognizable patterns. Data analysis answers yes to every one — which is precisely what makes it a field where AI is useful, and precisely what makes it automatable.
A data analyst is not protected by understanding AI, any more than a copywriter is protected by understanding language.
What actually separates a 6.0 from an 8.2 inside this field is the same thing that separates tracks everywhere else: judgment and accountability.
An ML engineer designing a system that will make decisions at scale rates 8.0 on novelty, because the failure modes are unprecedented and expensive. A senior data scientist telling an executive their strategy is unsupported by the data carries accountability that attaches to a person. An entry analyst producing a requested dashboard carries neither, and rates 4.0 on both.
The general lesson: being close to a technology is not the same as being protected from it. Ask what shape the work is — where the inputs come from, whether a human must answer for the output — not what the work is about.
Ranked by exposure, safest first:
Note that nothing in this field scores below 6.0. Data science has no physical component, no licensure and no structural protection of any kind. The entire range is set by seniority.
It depends heavily on which end of the field it points at, and most programs point at the exposed end.
The versions that hold up:
The version that does not: a program teaching Python, SQL, dashboarding and standard model fitting, pointed at an analyst job. That is training for the most automated work in the field.
Worth asking any program: what proportion of your graduates are in engineering or research roles versus analyst roles, and how has that shifted in three years?
This sampler tells you where data science stands. The full profile tells you what to do about it.
Most families are weighing two or three careers seriously, and a few more they haven’t ruled out. Pick the ones you need.
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